YOLOR[1]} is basically a unified network architecture, which combines the explicit knowledge (i.e. shallow layer activations) and the implicit knowledge (i.e. deeper layer activations) of a network. Hence, more than an object detection specific architecture, YOLOR is designed as a multi-task learning model. However, the model is operationalized for object detection and performs comparable to leading non-real-time models such as the swin transformer [2]}, but achieves this in real-time. Because the model includes an encoder design of its own, no external backbone such as ResNet-50 is implemented for this model. In our experiments, we utilize the version called “YOLOR-P6”, which is based on the “YOLOv4-P6-light” architecture.
